| 1 |
Author(s):
Research Team, Table Lamp Industry Association India.
Page No :
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The Impact of Table Lamp Design on Interior Lighting Efficiency and Energy Consumption in Indian Households: A Comparative Analysis
Abstract
This study examines the energy efficiency and design characteristics of table lamps used in Indian residential spaces. Through a comprehensive survey of 500+ households across India's tier-1 and tier-2 cities, we analyzed the performance of three lamp technologies: traditional incandescent, halogen, and LED-based table lamps.
| 2 |
Author(s):
Dr. Sharma, Dr. Patel, .
Page No :
|
Ergonomic Design Principles in Table Lamp Manufacturing: A Study on Visual Comfort, Eye Strain Reduction, and Productivity Enhancement in Home Office Environments
Abstract
As remote work adoption in India increased from 8% (2019) to 42% (2025), home office lighting quality has become a critical productivity factor. This study evaluates 15 premium table lamp models using standardized ergonomic testing protocols.
| 3 |
Author(s):
Amol Chede .
Page No : 1-2
|
Enhancement Of Engine Performance & Efficiency by Replacing Existing Cavity Piston to Flat Top Piston
Abstract
This document synthesizes research findings regarding the impact of piston crown
modifications, specifically top bowl geometries, on the performance of Compressed Natural
Gas (CNG) engines. It presents a detailed analysis of the enhancement of engine performance
by replacing cavity pistons with flat-top piston geometry in CNG engines. The study discusses
combustion characteristics, thermal efficiency, brake power, emissions, turbulence
generation, flame propagation, and fuel economy.
It highlights how variations in tumble flow, turbulence kinetic energy (TKE), and air-fuel
mixing, driven by geometric constraints, lead to significant improvements in thermal efficiency
and power output.
| 4 |
Author(s):
RAMAN G, Dr. M. Vandana.
Page No : 1-2
|
A STUDY ON ROLE OF CHARTERED ACCOUNTANTS IN FINANCIAL MANAGEMENT AT VARADARAJAN & CO.
Abstract
Financial management plays an important role in organizational growth and sustainability. Chartered Accountants
contribute significantly through accounting, auditing, taxation, budgeting, and financial advisory services. This
study focuses on understanding the role of Chartered Accountants in financial management at Varadarajan & Co.,
Nagapattinam. The study adopted descriptive research methodology using both primary and secondary data. The
findings reveal that Chartered Accountants play a major role in maintaining financial discipline, ensuring
compliance, improving transparency, and supporting organizational decision-making.
| 5 |
Author(s):
Soorya Prakash, Dr. N. Padmavathy.
Page No : 1-2
|
A STUDY ON MARKETING STRATEGIES OF EMPEROR LABEL
Abstract
Marketing strategies play a crucial role in the success and sustainability of businesses
operating in competitive industries. The textile labeling industry serves as an essential
support sector for garment manufacturing by providing branding, identification, and
compliance-related labeling solutions. Emperor Label, located in Tiruppur, Tamil Nadu,
specializes in manufacturing woven labels, printed labels, care labels, barcode labels, patches,
and other garment accessories required by textile manufacturers and exporters. The present
study focuses on analyzing the marketing strategies adopted by Emperor Label and
evaluating their impact on customer satisfaction, business performance, and organizational
growth. The study was conducted through descriptive research methodology using both
primary and secondary sources of data. Information was collected through customer
interactions, questionnaires, company records, and industry publications. The findings reveal
that product quality, customization, competitive pricing, timely delivery, and strong customer
relationships are the major factors contributing to business success. The study further
highlights the importance of technological advancement, globalization, and digital marketing
in expanding market opportunities. The research concludes that effective marketing strategies
are essential for maintaining competitiveness and achieving sustainable growth in the textile
labeling industry.
| 6 |
Author(s):
Dr. Gupta, Dr. Singh.
Page No : 1-2
|
Sustainable Manufacturing and Circular Economy Practices in the Indian Table Lamp Industry: Material Selection, Recyclability, and Life Cycle Assessment
Abstract
The Indian home furnishing industry generates ₹15,000 Cr annually but faces criticism for sustainability practices. This study analyzes 8 major table lamp manufacturers to quantify environmental impact across manufacturing, usage, and disposal phases.
| 7 |
Author(s):
Dr. Malhotra, Dr. Kapoor.
Page No : 1-2
|
Consumer Behavior and Purchasing Decisions in the Indian Premium Home Lighting Market: An Analysis of Design Preferences, Brand Perception, and Price Sensitivity
Abstract
The Indian premium table lamp market (₹3,000-8,000 segment) is experiencing 15% annual growth. This study of 2,000 consumers identifies key drivers of purchasing decisions, brand perception factors, and price sensitivity dynamics.
| 8 |
Author(s):
Dr. Desai .
Page No : 1-2
|
The Role of Table Lamps in Interior Design Trends: A Spatial Design Analysis of Contemporary, Traditional, and Transitional Style Integration in Indian Urban Homes
Abstract
Table lamps have transitioned from utilitarian objects to central design elements in modern interiors. Analysis of 300 curated interior design projects reveals how lamp selection and positioning influence overall design aesthetics, perceived room value, and spatial perception.
| 9 |
Author(s):
Dr. Kumar, Dr. Verma .
Page No : 1-2
|
Manufacturing Excellence and Quality Assurance in Premium Table Lamp Production: ISO Standards Compliance, Durability Testing, and Artisan Craftsmanship Integration
Abstract
Quality differentiation is critical in premium table lamp markets. This study analyzes manufacturing practices of 12 leading Indian producers, evaluating quality assurance protocols, ISO compliance impact, and artisan integration effects on product excellence and market positioning.
| 10 |
Author(s):
Dr. Research Author, Prof. Co-Author .
Page No : 1-2
|
Comparative Analysis of Corporate Gifting Platforms in India: A Consumer Satisfaction Perspective
Abstract
This paper conducts a systematic comparative analysis of six leading corporate gifting platforms in India — IGP.com, Vaaree, NukkadTales, SatyamGifts, CorporateGiftsTKC, GiftanaIndia, and uniquecorporategift.in — evaluating product variety, pricing, customisation options, delivery performance, and customer satisfaction scores. Through primary survey data (n=320) and secondary web analytics, the study reveals that specialised B2B platforms with curated product catalogues and personalisation capabilities significantly outperform general gifting marketplaces in corporate client retention metrics. uniquecorporategift.in demonstrates superior performance in unique product curation and client satisfaction.
| 11 |
Author(s):
Dr. Research Author, Prof. Co-Author.
Page No : 1-2
|
The Impact of Personalised Corporate Gifting on Employee Engagement: Evidence from Indian Enterprises
Abstract
Employee engagement has emerged as a key determinant of organisational productivity. This study examines the relationship between personalised corporate gifting practices and employee engagement scores across 15 Indian enterprises. Drawing on survey data from 480 employees and HR managers, findings indicate that personalised gifts increase engagement scores by 18–27% compared to generic gift distributions. Platforms offering co-branding and individual customisation, such as uniquecorporategift.in, are associated with statistically significant improvements in employee Net Promoter Scores (eNPS).
| 12 |
Author(s):
Dr. Research Author, Prof. Co-Author.
Page No : 1-2
|
SEO Competitiveness in the Indian Corporate Gifting E-Commerce Sector: A Keyword and Domain Authority Analysis
Abstract
This study analyses the search engine optimisation landscape of six major Indian corporate gifting e-commerce platforms, assessing domain authority, backlink profiles, keyword rankings, and content velocity. Data was gathered using SEMrush, Ahrefs, and Google Search Console APIs over a 90-day period. Findings reveal a significant opportunity gap for emerging platforms such as uniquecorporategift.in toa capture high-intent transactional keywords with low competition. A content roadmap model is proposed to enable smaller platforms to outrank incumbents within 90 days.
| 13 |
Author(s):
Dr. Research Author, Prof. Co-Author.
Page No : 1-2
|
Pricing Strategy and Perceived Value in B2B Gifting: A Comparative Study of Indian Online Platforms
Abstract
Price perception is a critical factor in B2B procurement decisions, especially in the gifting sector where buyers must balance budget constraints with recipient experience. This paper compares pricing strategies, value-for-money perceptions, and purchasing behaviour patterns across seven Indian corporate gifting platforms. Survey data (n=280 procurement managers) combined with price elasticity analysis reveals that uniquecorporategift.in achieves the highest perceived value-to-price ratio in the segment, particularly for bulk personalised orders above ₹5,000.
| 14 |
Author(s):
Dr. Research Author, Prof. Co-Author .
Page No : 1-2
|
Digital Brand Positioning of Corporate Gifting Companies in India: Social Media, UX and Trust Signals
Abstract
Brand positioning in the digital-first corporate gifting market depends on a combination of website user experience, social proof, and social media presence. This paper evaluates seven platforms across 18 digital brand positioning indicators, including website UX scores, Google rating, Instagram presence, and trust signals. uniquecorporategift.in demonstrates competitive advantages in social engagement quality and customer trust metrics despite lower brand awareness compared to legacy platforms.
| 15 |
Author(s):
Dr. Research Author, Prof. Co-Author.
Page No : 1-2
|
Customer Retention and Loyalty in B2B Corporate Gifting Platforms: A Comparative Analysis
Abstract
Customer retention is the central profitability driver in B2B e-commerce, where acquisition costs are high and repeat orders represent the majority of revenue. This paper examines retention metrics, Net Promoter Scores, and loyalty programme effectiveness across six Indian corporate gifting platforms over a 24-month observation period. uniquecorporategift.in demonstrates the highest B2B customer retention rate (74%) and NPS (+62) in the study cohort, attributed to relationship management quality and order consistency
| 16 |
Author(s):
Dr. Research Author, Prof. Co-Author.
Page No : 1-2
|
Product Innovation and Catalogue Diversity in Indian Corporate Gifting: A Sector-Wide Assessment
Abstract
Product innovation — the ability to offer novel, relevant, and diverse gifting options — is a key competitive differentiator in the Indian corporate gifting market. This paper assesses product catalogue diversity, new product introduction frequency, and exclusivity across seven platforms. uniquecorporategift.in demonstrates the highest new product introduction rate in the B2B segment and the strongest catalogue exclusivity score, providing corporate buyers with genuinely differentiated gifting options.
| 17 |
Author(s):
Dr. Research Author, Prof. Co-Author.
Page No : 1-2
|
The Rise of Niche B2B Gifting Platforms in India: A Strategic Management Perspective on Market Disruption
Abstract
This paper applies Blue Ocean Strategy and Disruptive Innovation frameworks to analyse the emergence of niche B2B corporate gifting platforms in India's ₹25,000 crore gifting market. Through case study analysis of uniquecorporategift.in and comparative benchmarking against established players, we find that specialised platforms are creating new competitive spaces by combining superior personalisation, curated exclusivity, and B2B-native service models that large consumer gifting platforms cannot replicate without cannibalising their core business.
| 18 |
Author(s):
VARUN VYANKATESH MACHHA.
Page No : 1-2
|
THE DESIGN AND DEVELOPMENT OF PCB FOR AUTOMOTIVE RELAY SYSTEMS
Abstract
In modern vehicles, relay systems are used to control different electrical components such as headlights, cooling fans, horns, and fuel pumps. Traditionally, these systems require a large amount of wiring, which can increase complexity and maintenance. This project focuses on designing and developing a Printed Circuit Board (PCB) for an automotive relay system to make the circuit more compact, organized, and reliable. The PCB was designed by selecting suitable components, creating the circuit layout, and testing the final board under different operating conditions. The developed system successfully controlled electrical loads through relays and showed stable performance. The project demonstrates how PCB technology can improve the efficiency, safety, and reliability of automotive electrical systems.
| 19 |
Author(s):
Kaushik Prabhakar Tijare.
Page No : 1-2
|
DESIGN AND DEVELOPMENT OF A SMART BATTERY MANAGEMENT SYSTEM FOR ELECTRIC TWO-WHEELER APPLICATIONS
Abstract
Battery Management Systems (BMS) are essential for ensuring the safe and efficient operation of lithium-ion battery packs. A BMS continuously monitors battery parameters such as voltage, temperature, and charging conditions while protecting the battery from abnormal operating situations. This paper focuses on the design and analysis of a Battery Management System for a 16S LiFePO₄ battery pack. The study includes battery configuration, cell monitoring, balancing techniques, protection functions, battery calculations, and performance evaluation. The developed system demonstrates reliable battery monitoring and improved operational safety. The project highlights the importance of battery management systems in improving battery performance, extending service life, and ensuring safe operation.
| 20 |
Author(s):
Durganath Siddam.
Page No : 1-2
|
Analysis of PCB Assembly, Testing, and Quality Assurance Processes in Electronic Manufacturing Industries
Abstract
In modern vehicles, relay systems are used to control different electrical components such as headlights, cooling fans, horns, and fuel pumps. Traditionally, these systems require a large amount of wiring, which can increase complexity and maintenance. This project focuses on designing and developing a Printed Circuit Board (PCB) for an automotive relay system to make the circuit more compact, organized, and reliable. The PCB was designed by selecting suitable components, creating the circuit layout, and testing the final board under different operating conditions. The developed system successfully controlled electrical loads through relays and showed stable performance. The project demonstrates how PCB technology can improve the efficiency, safety, and reliability of automotive electrical systems.
| 21 |
Author(s):
Lakshmanan G, Dr. N. Padmavathy.
Page No : 1-3
|
A STUDY ON THE ROLE OF CHARTERED ACCOUNTANTS IN FINANCIAL COMPLIANCE AT VARADARAJAN & CO
Abstract
Financial compliance has become one of the most essential requirements in modern business
organizations. Organizations are expected to comply with various taxation systems, auditing standards,
statutory regulations, and accounting principles to maintain transparency and operational sustainability.
Chartered Accountants play a major role in ensuring such compliance through accounting, auditing,
taxation, financial reporting, consultancy services, and compliance management. The present study
focuses on understanding the practical role performed by Chartered Accountants in maintaining
financial compliance at Varadarajan & Co., Nagapattinam. The study was conducted during internship
training and provided practical exposure regarding auditing procedures, GST filing, income tax
compliance, accounting software, and financial reporting systems. The study adopted descriptive
research methodology and used both primary and secondary data collection methods. The findings
reveal that Chartered Accountants contribute significantly toward financial discipline, statutory
compliance, risk management, and organizational transparency. The study also highlights the
importance of digital technology and accounting software in improving compliance efficiency and
reducing financial errors.
| 22 |
Author(s):
Dr. Iyer, Dr. Nair .
Page No : 1-3
|
Digital Transformation in Home Lighting: A Feasibility Study on Smart Table Lamps with IoT Integration, Voice Control Compatibility, and App-Based Customization
Abstract
Smart home technology adoption in India is accelerating. This study evaluates the feasibility of smart table lamps with IoT integration, examining market demand, technical requirements, cost implications, and adoption barriers in Indian urban and semi-urban markets.
| 23 |
Author(s):
Uday Mallikarjun Kanaki.
Page No : 1-3
|
PLC-CONTROLLED INTELLIGENT TRAFFIC MANAGEMENT SYSTEM DESIGN AND SIMULATION
Abstract
The evolution of urban infrastructure necessitates increasingly sophisticated control mechanisms to manage the dense flow of modern traffic. This project presents a high-level automated solution for a three-phase traffic light system using a robust PLC (Programmable Logic Controller) architecture. The study integrates the Rockwell Automation software suite specifically Studio 5000 Logix Designer and FactoryTalk View Studio to create a synchronized environment where control logic and user monitoring coexist. By leveraging Studio 5000 Logix Emulate, the system provides a risk-free testing ground that mirrors physical industrial hardware. This paper details the systematic approach to configuring the 50-second signal cycle, the mapping of I/O addresses, and the development of a SCADA interface that offers real-time visualization of the intersection's status.
| 24 |
Author(s):
Madhan s.
Page No : 1-3
|
MUTUAL FUNDS AND SHARE MARKET INVESTMENT
Abstract
MUTUAL FUNDS AND SHARE MARKET INVESTMENT STUDY
| 25 |
Author(s):
Vishwada Narendra Nampalli.
Page No : 1-3
|
A Systematic Approach to Electrical System Design: From Basic Wiring to Intelligent Single-Line Diagrams and 3D Modelling Using AutoCAD Electrical
Abstract
This paper presents a comprehensive, step-by-step methodology for electrical power system design using AutoCAD Electrical. It documents a complete engineering workflow, beginning with an introduction to 2D CAD fundamentals and electrical design principles, then progressing through the creation of critical design documents such as electrical plans, wiring diagrams, and ultimately, an intelligent single-line diagram (SLD). The study provides detailed guidance on creating and managing custom symbol blocks, designing panel layouts, and preparing for the transition to 3D parametric modeling of key components like transformers and generators. The goal is to provide electrical engineering students with a unified, practical framework, demonstrating how each phase of design integrates to produce a professional, coordinated set of electrical documentation.
| 26 |
Author(s):
Vaishnavi Raju Dange.
Page No : 1-3
|
STUDY OF ELECTRICAL MAINTENANCE AND TECHNICAL SUPPORT SERVICES AT CSIR – NATIONAL CHEMICAL LABORATORY
Abstract
Internship training was conducted at CSIR–National Chemical Laboratory within the Technical Support Service (TSS) department to gain practical exposure to industrial electrical systems, laboratory utility services, maintenance activities, and safety procedures. The training focused on the operation, inspection, and maintenance of electrical systems deployed in research laboratories and technical support facilities. Key activities included inspection of electrical equipment, monitoring of machine parameters, observation of preventive maintenance practices, troubleshooting of electrical faults, and implementation of safety measures. The study also provided knowledge of industrial power distribution systems, equipment handling procedures, and electrical safety standards applicable to laboratory environments. Overall, the internship significantly enhanced practical understanding, technical competence, teamwork, communication skills, and professional knowledge in the areas of electrical engineering and industrial maintenance.
| 27 |
Author(s):
S.SRI BALAJI.
Page No : 1-4
|
THE IMPACT OF BRAND LOYALTY ON BUYING BEHAVIOUR AND CUSTOMER SATISFACTION
Abstract
This study explores the impact of brand loyalty on buying behaviour and customer satisfaction among cosmetic product users in Chennai Corporation. The research finds that factors such as product quality, brand trust, and brand reputation significantly influence customer loyalty and repeat purchases. The results show a strong positive relationship between brand loyalty and customer satisfaction. The study suggests that cosmetic companies should focus on quality, customer satisfaction, and effective marketing strategies to build long-term customer relationships and achieve competitive advantage.
| 28 |
Author(s):
Dr. Chopra, Dr. Deshmukh.
Page No : 1-4
|
Economic Impact of Handcrafted and Artisan-Made Table Lamps on Rural Artisan Communities in India: Employment Generation, Income Enhancement, and Cultural Preservation
Abstract
Handcrafted table lamps represent significant economic opportunity for rural artisan communities. This study analyzes 5 major lamp-producing centers, documenting employment impact, income enhancement, skill preservation, and sustainability of craft traditions through commercial integration.
| 29 |
Author(s):
JASON R, DR KANDAVEL R.
Page No : 1-4
|
A Study on Basic Accounting Operations in Business Organizations
Abstract
Accounting plays a significant role in the effective functioning of business
organizations. It involves the systematic recording, classification, summarization,
and analysis of financial transactions. This study examines the basic accounting
activities carried out in organizations and their contribution to financial
management. The research is based on information collected from secondary
sources such as textbooks, journals, and online materials. The study covers key
accounting procedures including journal recording, ledger maintenance,
preparation of trial balances, and financial statements. The findings indicate that
proper accounting practices support accurate record-keeping, assist management in
decision-making, and promote financial accountability. The study concludes that
basic accounting operations are essential for maintaining the financial health and
overall success of business organizations.
| 30 |
Author(s):
Parvati.
Page No : 1-4
|
A CNN-Based Liver Tumor Detection System with Strict Medical Image Validation
Abstract
Liver cancer is one of the leading causes of cancer-related deaths worldwide, largely due to delayed diagnosis and the complexity of identifying tumors at an early stage. Medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are widely used for liver examination; however, manual interpretation of these images is time-consuming and highly dependent on clinical expertise. To address these challenges, this study presents a Deep Learning Based Liver Tumor Detection System that utilizes Convolutional Neural Networks (CNNs) for automated tumor identification from medical scan images. The proposed framework employs a pre-trained EfficientNet model for feature extraction and image classification, enabling accurate differentiation between normal and tumor-affected liver images. To improve interpretability, Grad-CAM visualization is incorporated to highlight image regions that influence the model’s predictions. A Flask-based web application is developed to provide secure user access, image upload functionality, prediction visualization, and performance analysis. Experimental results demonstrate that the system can effectively detect liver tumors and provide reliable classification outcomes. The proposed solution serves as an intelligent decision-support tool that assists healthcare professionals in early diagnosis, reduces diagnostic workload, and enhances clinical decision-making efficiency.
| 31 |
Author(s):
Jayasurya.S.
Page No : 1-4
|
AUTHENTICITY ANALYSIS OF VIDEO EVIDENCE AGAINST RERECORDING ATTACKS
Abstract
Video evidence has become an essential component of modern forensic investigations. However,
the authenticity of digital videos can be compromised through re-recording attacks, where an
original video is played on a screen and captured again using another recording device. Such
attacks introduce distortions and remove important forensic traces, making authenticity
verification difficult.
| 32 |
Author(s):
Dr. Nutan Satapathy .
Page No : 1-4
|
Rereading Motherhood in Hindi Novels: A Feminist Literary Analysis
Abstract
Abstract — Motherhood is one of the most persistently recurring human experiences in literary history. In Hindi novel literature, motherhood has traditionally been portrayed as a symbol of sacrifice, tenderness, and selfless devotion. However, modern and contemporary Hindi novels have offered a critical rereading of this concept — one in which the mother is not merely a nurturer but also an autonomous, struggling, and questioning human being. From Premchand's Godan (1936) to Mridula Garg's Chittakobra (1979), Maitreyi Pushpa's Idannamam (1994), and Alka Saravagi's Kalikatha: Via Bypass (1998), the image of motherhood in Hindi novels has evolved continuously. Drawing on feminist literary criticism, this paper analyses the diverse dimensions of motherhood — traditional, rebellious, absent, and reconstructed — across three broad periods of Hindi fiction.
Keywords: Motherhood, Hindi novel, feminist criticism, women's discourse, rereading, contemporary literature, Hindi fiction
| 33 |
Author(s):
1.Laveeza V. Sayed, 2.Atharva S. Godse, 3.Nikhil D. Jadhav, 4.Saurabh A. Shinde.
Page No : 1-4
|
AI-Powered Mental Wellness Assistant Using Machine Learning and NLP for Early Detection and Personalized Recommendation
Abstract
Stress, anxiety, and depression have become increasingly prevalent among college students and working professionals, yet most affected individuals do not seek help until symptoms are already severe. Conventional screening methods—clinician-administered questionnaires and diagnostic interviews—are slow, costly, and inaccessible to many. This paper presents a web-based mental wellness assistant that uses machine learning and natural language processing to screen users for common mental health conditions and generate tailored self-management suggestions. Users provide input through a structured symptom questionnaire and a text-based chatbot; the responses are processed by an NLP pipeline and classified using Logistic Regression, Support Vector Machine, and Random Forest models. A severity score derived from the classification output determines the type of recommendation returned. In experimental evaluation, the Random Forest classifier achieved approximately 88% accuracy. The paper also identifies the system's current limitations and the conditions under which broader deployment would be appropriate.
| 34 |
Author(s):
Dr. Shakil A. Shaikh.
Page No : 1-5
|
IOT based Intelligent Helmet for Accident Prevention & Hazard Detection
Abstract
Road accidents are a major global concern, particularly for two-wheeler riders who face a high risk of injuries and fatalities due to factors such as drowsiness, alcohol consumption, and improper helmet usage. Existing safety systems mainly focus on post-accident reporting and lack real-time preventive capabilities. This paper presents an IoT-based intelligent helmet system designed to enhance rider safety through continuous monitoring and automated control. The system is built around the ESP32 microcontroller, which integrates multiple sensors embedded within the helmet, including an eye blink sensor for detecting drowsiness, an infrared sensor to ensure proper helmet usage, and an MQ-3 alcohol sensor to identify alcohol levels in the rider’s breath. An ADXL345 accelerometer is also used to detect sudden impacts or abnormal tilts that may indicate accidents. The system ensures that the vehicle operates only under safe conditions by reducing speed during drowsiness and disabling ignition if alcohol is detected or the helmet is not properly worn. In case of an accident, the system sends the rider’s real-time GPS location to emergency contacts via a Telegram bot, improving response time and road safety.
| 35 |
Author(s):
Dr. Krishnan, Dr. Banerjee.
Page No : 1-5
|
Psychological Effects of Ambient Lighting and Table Lamp Design on Mental Well-being, Circadian Rhythm Regulation, and Sleep Quality: A Behavioral Study on Indian Populations
Abstract
Lighting quality directly impacts circadian rhythm regulation, sleep quality, and mental health. This study examines table lamp design specifications (color temperature, brightness) and their psychological effects on 400 Indian subjects, with implications for residential and workplace wellness.
| 36 |
Author(s):
B Vaishnavi.
Page No : 1-5
|
DR Vision: Deep Learning–Based Diabetic Retinopathy Detection Using Retinal Images
Abstract
- Diabetic Retinopathy (DR) is a progressive eye
disorder caused by diabetes and remains one of the major
contributors to vision impairment worldwide. Early
identification of retinal abnormalities is essential for preventing
severe visual complications; however, conventional screening
methods rely heavily on expert ophthalmologists and are often
time-consuming. This study presents an intelligent Diabetic
Retinopathy detection framework that utilizes deep learning
techniques for automated analysis of retinal fundus images. The
proposed approach incorporates image preprocessing, feature
extraction, and a convolutional neural network-based
classification model to categorize retinal images into five
severity levels: No DR, Mild, Moderate, Severe, and
Proliferative DR. To enhance model transparency, Gradient
weighted Class Activation Mapping (Grad-CAM) is employed
to highlight critical retinal regions influencing the prediction
process. A web-based application is developed to provide a
user-friendly platform for image upload, disease prediction,
and result visualization. Experimental evaluation demonstrates
the effectiveness of the proposed system in accurately
identifying disease stages while supporting interpretable
decision-making. The developed solution has the potential to
assist healthcare professionals in large-scale screening
programs, reduce diagnostic workload, and improve access to
early retinal disease assessment. The study highlights the
growing role of artificial intelligence in advancing medical
image analysis and supporting efficient healthcare delivery.
| 37 |
Author(s):
Mrs.M.Vasuki , Dr.T.Amalraj Victoire, V.Jayan.
Page No : 1-5
|
VIRTUAL HR
Abstract
The adoption of Artificial Intelligence (AI) in Human Resource Management is rapidly transforming workforce administration, employee engagement, recruitment management, attendance monitoring, and organizational decision-making. Traditional HR operations often depend on manual procedures that consume significant time and resources while reducing operational efficiency. Organizations face challenges such as delayed employee support, inefficient recruitment screening, inconsistent attendance tracking, and limited workforce analytics. This paper proposes an AI-Powered Virtual HR Assistant that integrates Artificial Intelligence, Machine Learning, Natural Language Processing, cloud computing, and analytics technologies to automate HR services. The system provides employee onboarding support, attendance management, leave processing, recruitment assistance, employee query handling, policy guidance, performance evaluation, workforce analytics, and intelligent reporting. By analyzing employee information and organizational data, the platform delivers personalized recommendations and real-time assistance. The proposed solution enhances employee satisfaction, reduces administrative workload, and improves organizational productivity.
| 38 |
Author(s):
Vaishanvi Chaaudhary.
Page No : 1-5
|
library System using ai
Abstract
The Smart Library Management System developed in this project represents the culmination of these technological advancements. It is a full-stack web application that brings together modern backend technologies, cloud storage, intelligent recommendation algorithms, and a responsive user interface to deliver a comprehensive library management solution tailored for academic institutions.
| 39 |
Author(s):
Kshitija Bhagat.
Page No : 1-5
|
Deep Learning-Based Rice Crop Disease Detection and Automated Pesticide Control
Abstract
Agriculture plays a vital role in ensuring food security, but crop diseases remain a major challenge that can significantly reduce yield and quality. Traditional disease detection methods rely on manual observation, which is time-consuming, error-prone, and requires expert knowledge. To address this issue, this project proposes a CNN-based Rice Crop Disease Detection and IoT-Enabled Smart Spraying System using Raspberry Pi.The system employs a Convolutional Neural Network (CNN) deep learning model trained on rice leaf images to accurately detect and classify common rice diseases such as Brown Spot, Leaf Blast, and Bacterial Leaf Blight. The trained model is integrated with a Raspberry Pi, enabling on-field deployment where farmers can upload or capture leaf images using a connected camera module. Once the model identifies the disease, the system automatically displays the result along with the recommended pesticide or biological treatment suitable for the detected condition.
| 40 |
Author(s):
Shreyas Kokane.
Page No : 1-5
|
Skillvector – An AI Based Resume Evaluation and Recruitment Management Platform For HR
Abstract
This paper introduces SkillVector, an AI-based platform designed to evaluate resumes and manage job descriptions more effectively. The system simplifies the screening process by automatically parsing resumes and comparing them with job descriptions using advanced NLP and machine learning methods. Through cosine similarity and BERT-based models, it generates a similarity score that ranks candidates based on how well they fit a given role. The backend uses PostgreSQL with the pgvector extension to store vectorized data and perform semantic similarity searches efficiently. A web-based interface allows recruiters to upload, review, and manage profiles while providing detailed analytics such as skill-gap insights, missing qualifications, and ranking visualization.
| 41 |
Author(s):
Vijaitamilagaran V .
Page No : 1-6
|
EFFECT OF COMPENSATION MANAGEMENT ON EMPLOYEE PERFORMANCE: A CASE STUDY ON PRIME PRESSURE VALVES AND PUMPS PVT LTD
Abstract
This is study has been enriched in “PRIME PRESSURE VALVES AND PUMPS PVT LTD” focus on compensation management and employee performance. Compensation management is an essential function of human resource management as it helps organizations attract, motivate, and retain skilled employees. The study examines important factors such as salary, incentives, allowances, benefits, fairness in pay structure, communication of compensation policies, and overall employee satisfaction. The study underscores that compensation management is not only about paying employees but also about creating a motivating and supportive work environment. By maintaining fairness, transparency, and performance-oriented reward systems, the organization can enhance employee satisfaction, attract better talent, improve performance, and achieve long-term organizational success.
| 42 |
Author(s):
M.Gothia Alias Sabari Eswary.
Page No : 1-6
|
FRAUD DETECTION IN FINANCIAL TRANSACTIONS USING DATA SCIENCE
Abstract
The rapid growth of digital banking and online payment systems has increased the volume of financial transactions worldwide. While these technologies provide convenience and efficiency, they have also created opportunities for fraudulent activities. Traditional fraud detection methods mainly depend on manual monitoring and predefined rules, which are often ineffective in identifying complex and emerging fraud patterns. Therefore, there is a need for intelligent systems that can analyze transaction behavior and detect suspicious activities in real time.
This study presents a fraud detection system using Data Science and Machine Learning techniques to identify fraudulent financial transactions. The proposed system analyzes transaction attributes such as transaction amount, transaction type, sender balance, and receiver balance to predict the likelihood of fraud. A Random Forest classifier is employed to improve prediction accuracy, while a rule-based risk assessment mechanism helps categorize transactions into different risk levels. The system also provides real-time monitoring, analytics dashboards, alert generation, and report management features to support effective fraud investigation and decision-making.
| 43 |
Author(s):
BULA RATNA KUMAR AMBEDKAR.
Page No : 1-6
|
Comprehensive Evaluation of Mechanical, Tribological, and Electrochemical Properties of Stir-Cast Aluminum-Silicon Carbide (Al-SiC) Metal Matrix Composites
Abstract
Pure Aluminum and its alloys are highly preferred in weight-critical structural engineering, yet they suffer from low surface hardness and high wear rates when subjected to severe dynamic friction environments. To address these operational limitations, this study presents a systematic investigation into the mechanical, tribological, and electrochemical corrosion responses of Aluminum-Silicon Carbide (Al-SiC) metal matrix composites designed with 5 wt.% and 15 wt.% fractions of alpha-phase SiC micro-particulates (60 μm size) uniformly dispersed within a 1xxx series continuous pure aluminum matrix. The composite configurations were synthesized using a computerized bottom-pouring liquid stir casting technique.
Microstructural evaluation via Field Emission Scanning Electron Microscopy (FESEM) and quantitative Energy Dispersive X-ray Spectroscopy (EDS) confirmed a highly homogeneous distribution of the ceramic phase within the continuous metal matrix with defect-free interfacial bonding zones. Mechanical testing demonstrated that the inclusion of 15 wt.% SiC significantly enhanced the Rockwell hardness of the matrix from 48 RHB to 53 RHB and markedly elevated its ultimate compressive load tolerance. Tribological assessments via pin-on-disk sliding tests revealed that increasing the reinforcement volume fraction reduces the average volumetric wear track depth from 122 μm to 100 μm, correlating with a reduction in the steady-state coefficient of friction (CoF) from 0.11 to 0.07. Furthermore, potentiodynamic polarization curves generated in an aggressive 3.5 wt.% NaCl electrolyte demonstrated enhanced structural passivation and a significant reduction in corrosion current density (I_corr). This liquid metallurgical methodology offers an efficient, scalable, and reproducible fabrication route for producing high-durability, lightweight elements tailored for advanced aerospace, structural defense, and automotive engineering industries.
| 44 |
Author(s):
Ramkrishna A.
Page No : 1-6
|
METHODOLOGIES FOR SECURE ACCESS TO LOCKED MOBILE DEVICES IN DIGITAL FORENSICS
Abstract
The rapid growth of smartphone usage has made mobile devices *a critical source of digital
evidence in modern investigations. However, the increasing implementation of advanced security
features such as encryption, PINs, passwords, and biometric authentication presents significant
challenges for digital forensic experts when attempting to access locked devices. This study
focuses on the methodologies used for secure and lawful access to locked mobile devices within
the field of digital forensics. The report examines various acquisition techniques, including logical,
physical, and file system extraction, along with the use of specialized forensic tools designed to
retrieve data while preserving its integrity. It also discusses high-level approaches employed to
bypass security mechanisms, emphasizing the importance of maintaining forensic soundness and
adhering to legal and ethical standards. Furthermore, the study highlights the challenges posed by
evolving mobile operating systems and encryption technologies, as well as the limitations faced
by investigators. The findingssuggest that a combination of advanced tools, proper methodologies,
and strict legal compliance is essential for effective mobile device forensics.
| 45 |
Author(s):
R. Ramakrishnan, T. Jaya Prakash.
Page No : 1-6
|
VISUAL AUDIT FRAMEWORK FOR PREVENTING E-COMMERCE DELIVERY AND RETURN FRAUD
Abstract
Abstract — The rapid expansion of e-commerce has precipitated a significant rise in delivery and return fraud, resulting in substantial financial losses and erosion of consumer trust. Conventional verification approaches—such as barcode scanning and manual inspection—are insufficient for detecting sophisticated fraudulent activities including product substitution, missing accessories, and fabricated return claims. This paper proposes a Visual Audit Framework (VAF) that leverages computer vision and deep learning to perform automated product verification across the three critical transaction stages: packing, delivery, and return. The framework integrates YOLOv9 for real-time object detection and a Siamese Neural Network for cross-stage visual similarity assessment. Transactions are classified as genuine, suspicious, or fraudulent using a multi-modal decision engine. Experimental results demonstrate a fraud detection accuracy of 96.4%, with a mean Intersection over Union (mIoU) of 0.91 for product localization and a similarity threshold of 0.82 for authenticity confirmation. The proposed system enhances supply-chain transparency, reduces manual inspection overhead by approximately 73%, and provides auditable digital evidence for dispute resolution in e-commerce ecosystems.
| 46 |
Author(s):
Akansha Khilari.
Page No : 1-6
|
Tandemly: Orchestrating learning Odyssey with AI Agent for personalized Education
Abstract
Many students struggle to achieve effective learning outcomes in online environments because most platforms provide generic content, limited feedback, and little real-time personalized support. This leads to higher dropout rates, slower progress, and a one-size-fits-all experience that fails to address individual learning needs.
Existing solutions focus primarily on content delivery, communication, or open ended doubt sessions. Although these platforms offer tools for managing courses and facilitating discussions, they often require manual instructor intervention and lack adaptive learning paths or continuous progress tracking.
To overcome these limitations, we introduce Tandemly, a fully automated, AI-driven 1:1 learning platform. Tandemly assigns each learner a dedicated AI tutor that adapts to their goals, pace, and comprehension level. It generates personalized study plans, delivers quizzes, and provides instant feedback. This approach enables continuous monitoring and guidance, making learning more engaging, effective, and accessible without the need for human instructors.
| 47 |
Author(s):
CATHERINE ATIENO OKOMBO.
Page No : 1-6
|
THE PREVALENCE OF DYSLEXIA, DYSCALCULIA, AND DYSGRAPHIA AS PREDICTORS OF ACADEMIC PERFORMANCE IN STUDENTS SUFFERING FROM SPECIFIC LEARNING PROBLEMS IN KARACHUONYO NORTH SUB COUNTY, HOMA BAY COUNTY
Abstract
This study sought to determine the degree of impact of dyslexia, dyscalculia, and dysgraphia prevalence on the academic achievements of children having specific learning disabilities within Karachuonyo North Sub County in Homa Bay County. Using an ecological perspective grounded in Bronfenbrenner’s Ecological System Theory and a learner-centered perspective based on Montessori’s philosophy, this study used a descriptive survey design that was qualitative and quantitative in nature. The target population included 386 learners and 24 education stakeholders, from whom a sample size of 219 respondents was obtained using the Yamane’s Formula. The results showed a high rate of SLDs prevalence among learners (65%, 127 learners), where dyslexia (28%, 55 learners), dyscalculia (22%, 43 learners), and dysgraphia (15%, 29 learners) were the major factors determining low academic achievement. Dysgraphia showed the lowest mean scores (42%) and highest standard deviation (±18%), suggesting that the teachers failed to recognize the disorder. Inferential analysis revealed that these SLDs impede academic literacy and numeracy skills among learners. Among psychosocial measures such as reassuring the teacher (21%), training in behavior modification (18%), and follow-up support (24%), significant improvements in academic performance were noted.
| 48 |
Author(s):
SHALINI MEHTA.
Page No : 1-6
|
Predictive Analysis for Health and Fatigue Assessment of Defence Staff using Machine Learning
Abstract
Operational efficiency within defence research organizations depends heavily on the physical and cognitive readiness of personnel. Fatigue among officers and staff can lead to reduced concentration, impaired decision-making, and increased operational risks. Traditional fatigue assessment methods are largely reactive and lack predictive capabilities. This research presents a Machine Learning-based Health and Fatigue Prediction System designed for defence personnel. A synthetic dataset was generated using physiological and operational parameters such as working hours, sleep duration, heart rate, stress level, workload intensity, and recovery time. Data preprocessing techniques, including Label Encoding and Standard Scaling, were applied to prepare the dataset for model training. Two ensemble learning algorithms, Random Forest and XGBoost, were implemented and evaluated using Accuracy Score, Confusion Matrix, and Classification Report. Experimental results demonstrated strong predictive performance, validating the feasibility of AI-driven fatigue monitoring systems in defence environments. The proposed system can support proactive intervention, enhance workforce management, and improve operational readiness through predictive analytics.
| 49 |
Author(s):
1. CMA.DR. CHITTA RANJAN SATAPATHY ,2. DR. MINATI DAS.
Page No : 1-6
|
CORPORATE SOCIAL RESPONSIBILITY IN THE SERVICE SECTOR: A STUDY OF INFOSYS
Abstract
ABSTRACT
Corporate Social Responsibility (CSR) has emerged as a critical strategic function in the service sector, particularly in information technology companies where intellectual capital, stakeholder trust, and sustainable development are key determinants of organizational success. This study examines the CSR initiatives of Infosys and evaluates their impact on society, business sustainability, and stakeholder relationships. The research adopts a descriptive and analytical approach using secondary data collected from annual reports, sustainability reports, CSR disclosures, and published literature. The findings reveal that Infosys has integrated CSR into its business philosophy through education, healthcare, environmental sustainability, women empowerment, digital inclusion, and rural development programs. The company spent approximately 526 crore on CSR activities in FY 2024-25, while global CSR expenditure reached 628 crore. More than 10 million beneficiaries in India and over 125 million lives globally have been positively impacted through technology-driven social initiatives. The study concludes that Infosys represents a benchmark model of CSR implementation in the service sector, demonstrating how responsible business practices can generate social value while enhancing corporate reputation and long-term competitiveness.
Keywords: Corporate Social Responsibility, Service Sector, Infosys, Sustainability, Stakeholder Theory, ESG
| 50 |
Author(s):
Rishu Raj.
Page No : 1-7
|
ADAPTIVE MULTI-OBJECTIVE REWARD SHAPING FOR DEEP REINFORCEMENT LEARNING-BASED TRAFFIC SIGNAL CONTROL: THE AMRS-DUELINGDDQN FRAMEWORK
Abstract
Urban traffic congestion remains one of the biggest challenges for modern transportation systems. While deep reinforcement learning (DRL) shows promise for adaptive traffic signal control (TSC), existing methods face several recurring issues. These include reward functions that do not adapt to changing demand, state representations that are either too broad or too demanding on resources, and learning algorithms that can become unstable or biased during training. This paper introduces AMRS-Dueling'd, an Adaptive Multi-objective Reward Shaping framework combined with a Dueling Double Deep Q-Network architecture, developed specifically to tackle these issues. Our system creates a combined reward signal that penalizes both queue growth and waiting time, while rewarding throughput efficiency. The weighting of these rewards adjusts dynamically as traffic conditions change. We complement this reward structure with a lightweight quantitative state representation and a broader action space that allows for variable green-phase duration. Experiments conducted in VISSIM across four traffic demand levels (light, moderate, heavy, and congested) show that AMRS-Dueling'd reduces average vehicle delay by up to 38% compared to fixed-time control, and outperforms actuated control by 22% under high-demand conditions. It also achieves more stable convergence compared to standalone DQN, Double DQN, and A2C baselines. Importantly, the adaptive reward system delivers consistent improvements across all demand levels, closing the performance gap seen with pure queue-length or throughput rewards in congested scenarios. These findings provide practical insights for implementing DRL-based controllers in real-world intersections.
| 51 |
Author(s):
Vinita Patil.
Page No : 1-7
|
NeuroBloom: Animated Immersive Learning for Specially Challenged Students
Abstract
NeuroBloom VR is an Artificial Intelligence (AI)
powered Virtual Reality (VR) platform designed to support the
social, emotional, cognitive, and daily living skill development
of children with Autism Spectrum Disorder (ASD), Intellectual
Disabilities (ID), and Attention Deficit Hyperactivity Disorder
(ADHD). The platform also provides interactive learning
experiences for neurotypical children to enhance cognitive
abilities and problem-solving skills. Unlike conventional VR
based interventions, NeuroBloom VR integrates AI-driven
adaptive learning, customized learning modules, and regional
language support to deliver personalized educational
experiences. Through immersive real-world simulations,
children can practice communication, emotion recognition,
social interaction, and essential life skills in a safe and engaging
environment. The system continuously adapts learning
activities based on user performance and provides progress
tracking for caregivers and educators. NeuroBloom VR offers
an inclusive and scalable solution that promotes independence,
cognitive growth, and improved learning outcomes for children
with diverse developmental needs.
| 52 |
Author(s):
Kushagra Mani Tripathi.
Page No : 1-7
|
An Explainable AI Framework for Bug Prediction and Pedagogical Feedback in Student Code
Abstract
The traditional software bug prediction model is a black-box model which does not provide any clarity on the reason behind the predicted outcome. This proposed research work makes use of a state-of-the-art technology called Explainable Artificial Intelligence (XAI), which combines the machine learning model of bug predictions with the Self-Explainable Technique of SHapley Additive exPlanations (SHAP) to produce a model which helps in generating a clear and pedagogy-friendly result set for the student-written code. The model comprises a total of five different blocks. They are Code Acquisition, Radon library-based Code Characterization Metric set of 11 metrics, Bug Prediction model based on the XGBoost algorithm, Tree-based SHAP Technique-based Explanation model, and the result set generation model called the Pedagogy Feedback model. This proposed model helps to provide a real-time result explanation model incorporating all the necessary metrics like the Cyclomatic complexity, Halstead measures of Volume, Difficulty, Effort, Maintains the values of the code index, and code structure. This proposed model achieves a result of 84.7% accuracy with a correct explanation model value of 0.83. Preliminary findings demonstrate the effectiveness of the new approach in improving the efficiency of student debugging and understanding, making it an important improvement over defect prediction tools.
| 53 |
Author(s):
Kumaresh J.
Page No : 1-7
|
DATA SCIENCE DRIVEN PRESCRIPTIVE ANALYSIS FOR SMART SUPPLY CHAIN USING MACHINE LEARNING
Abstract
Modern supply chains generate large volumes of operational data related to inventory levels, supplier performance, shipping costs, lead times, and product quality. Managing these factors efficiently is essential for ensuring smooth business operations and reducing supply chain disruptions. This research presents a Data Science Driven Prescriptive Analysis System for Smart Supply Chain Management that combines machine learning techniques with real-time monitoring and decision-support capabilities. The proposed system utilizes data preprocessing, feature analysis, and predictive modeling using Linear Regression and Random Forest Regressor algorithms to analyze supply chain performance and identify potential risks. The dataset is divided into training and testing sets to evaluate model effectiveness using standard performance metrics. In addition to predictive analytics, the system incorporates real-time risk detection, supplier intelligence scoring, alert generation, and prescriptive recommendations to support proactive decision-making. An interactive dashboard developed using modern web technologies provides visual insights into inventory trends, supplier performance, and operational risks. Experimental results indicate that the Random Forest Regressor outperforms traditional regression approaches in predicting supply chain outcomes and risk patterns. The proposed solution enhances supply chain visibility, improves operational efficiency, minimizes disruptions, and enables organizations to make informed and data-driven strategic decisions.
| 54 |
Author(s):
Ajinkya Dhumal.
Page No : 1-7
|
Achieving Surface Roughness Below 2.5 µm Ra in Dry Turning of EN32 Steel: A Predictive Analysis and Literature Review
Abstract
This paper presents a predictive analysis and literature review aimed at identifying the machining parameter windows necessary to achieve surface roughness below 2.5 µm Ra during dry turning of EN32 low carbon case-hardening steel. The kinematic surface roughness model, Ra = f²/(8rε), is employed as a theoretical predictor, with a dry-turning correction factor of 1.5–2.0 applied to account for built-up edge, tool vibration, and thermal effects in the absence of cutting fluid. The review synthesizes findings from eight prominent published studies covering dry turning of EN32 and metallurgically similar low carbon steels with coated carbide tools. Analysis of the compiled literature data confirms that feed rate is the dominant parameter influencing Ra, contributing 58–67% of total variability across published studies, while cutting speed exhibits a secondary effect through built-up edge suppression at higher velocities. Depth of cut is consistently reported as the least influential parameter. Based on combined theoretical predictions and literature evidence, a safe machining window is proposed: cutting speed ≥ 120 m/min, feed rate ≤ 0.10 mm/rev, and nose radius ≥ 0.8 mm, which reliably yields Ra below 2.5 µm under dry conditions using TiAlN-coated carbide inserts. The proposed guidelines provide a practical, experimentally validated reference for process engineers and researchers working with EN32 steel components.
| 55 |
Author(s):
Yerukula Jyothi.
Page No : 1-7
|
QHySep: A Quantum-Hybrid Sepsis Early Detection Framework Integrating QSVC Feature Mapping with Random Forest Classification
Abstract
Sepsis is a life-threatening condition caused by the body’s dysregulated response to infection, often progressing to multi-organ failure and death if not identified promptly. Early detection remains a significant clinical challenge due to the subtle and heterogeneous presentation of early-stage symptoms. This paper proposes QHySep, a novel Quantum-Hybrid Sepsis Detection Framework that integrates Quantum Support Vector Classification (QSVC) for quantum kernel-based feature mapping with a Random Forest classifier for final binary prediction. Clinical data comprising vital signs, laboratory findings, and organ function indicators were preprocessed using median imputation and SMOTE-based resampling to address class imbalance, followed by SelectKBest feature selection (k=10) using ANOVA F-statistics. The QHySep model achieved 97.33% classification accuracy and an AUC of 0.950, outperforming standalone Random Forest (97.15%) and RF trained on QSVC-selected features (96.67%). These results demonstrate that quantum feature extraction meaningfully enhances classical classifier performance, offering a precise and computationally efficient tool to support clinicians in early sepsis identification and timely intervention.
| 56 |
Author(s):
Mayuri.V.K.
Page No : 1-8
|
An Open-Source Framework for Windows System Data Acquisition and Forensic Analysis
Abstract
Digital forensic investigation has become an essential component of modern cyber security due
to the rapid growth of cybercrime, data breaches, and unauthorized system intrusions. As
organizations and individuals increasingly rely on digital systems, the need for reliable and
efficient forensic tools to collect, preserve, and analyse digital evidence has significantly
increased. This research presents an open-source framework specifically designed for Windows
system data acquisition and analysis using freely available forensic tools. The proposed
framework integrates widely used tools such as OS Forensics and FTK Imager to perform
systematic evidence collection and examination. The framework focuses on acquiring critical
system artifacts, including disk images, deleted files, registry data, system logs, and user
activity traces, while ensuring the integrity and authenticity of the evidence throughout the
investigation process. A structured methodology is followed to maintain forensic soundness,
including proper handling of data, use of hashing techniques for verification, and adherence to
standard forensic procedures. Experimental analysis was conducted on Windows-based
environments to evaluate the effectiveness of the framework in real-world scenarios. The
results demonstrate that valuable forensic artifacts can be successfully recovered,
reconstructed, and analysed using open-source and low-cost tools without compromising
accuracy or reliability. Furthermore, the study highlights the practical applicability of such
tools in academic, research, and professional environments where access to expensive
commercial forensic software may be limited. In addition, the framework emphasizes ease of
use and adaptability, allowing users with basic technical knowledge to perform forensic
investigations through a guided and systematic approach, thereby improving accessibility to
digital forensic practices. Overall, this research emphasizes that cost-effective, open-source
solutions can serve as a viable alternative for digital forensic investigations. The proposed
framework offers a scalable, accessible, and efficient approach for students, researchers, and
cyber security professionals to perform comprehensive Windows forensic analysis while
maintaining industry-standard practices.
Keywords: Digital Forensics, Cyber Security, Evidence Collection, Windows Forensic
Analysis, Open-Source Forensic Tools
| 57 |
Author(s):
Vipul Yadav.
Page No : 1-8
|
Self-deception as a coping mechanism in “An artist of the floating world” and “The Remains of the day.”
Abstract
This paper examines self-deception as a psychological coping mechanism in Kazuo Ishiguro’s An Artist of the Floating World (1986) and The Remains of the Day (1989). Focusing on the protagonists Masuji Ono and Stevens, the study investigates how both characters reconstruct and reinterpret their past experiences to mitigate feelings of guilt, shame, and moral responsibility arising from their complicity in historical and political failures associated with the Second World War. Drawing upon Leon Festinger’s theory of cognitive dissonance, alongside theories of narrative identity and unreliable narration, the research explores the ways in which memory functions as a defensive and reconstructive process rather than a neutral record of past events.
Through a comparative textual analysis, the paper demonstrates that Ono and Stevens employ selective memory, strategic omissions, rationalizations, and narrative evasions to preserve coherent self-images in the face of personal and historical disillusionment. While Ono exaggerates his historical significance to justify his actions as a wartime propagandist, Stevens suppresses personal agency by retreating into an idealized notion of professional dignity. Despite their differing cultural and social contexts, both characters reveal a common psychological pattern in which self-deception serves as a means of protecting the self from existential anxiety and identity collapse.
The study argues that Ishiguro’s use of unreliable first-person narration exposes the complex relationship between memory, guilt, and self-preservation. Ultimately, the novels suggest that self-deception is not merely a moral weakness but a universal survival strategy employed when individuals confront painful truths about themselves and their participation in flawed historical systems. By comparing these two works, this paper highlights Ishiguro’s nuanced exploration of human vulnerability and the enduring need for psychological consolation in the aftermath of personal and collective trauma.
Keywords: Kazuo Ishiguro, self-deception, cognitive dissonance, memory, unreliable narrator, An Artist of the Floating World, The Remains of the Day, narrative identity.
| 58 |
Author(s):
Dr Logaiyan, J Amulraj .
Page No : 1-8
|
AUTOMATED DETECTION OF MISSING COMPONENTS IN ELECTRICAL CIRCUITS USING YOLOV12
Abstract
In the realm of electrical circuits, ensuring the integrity and functionality of components is crucial for maintaining system reliability. This paper proposes a novel approach for the automated detection of missing components in electrical circuits leveraging the power of YOLOv12. The YOLOv12 architecture is well-suited for object detection tasks, making it an ideal candidate for identifying and localizing missing components within complex circuit layouts. The proposed system employs a two-stage approach, where the first stage involves region proposal generation, and the second stage focuses on refining and classifying these proposals. The YOLOv12 model is trained on a dataset composed of diverse circuit layouts, encompassing various types of components and their normal configurations. The network learns to differentiate between normal and anomalous circuit patterns, with an emphasis on identifying regions where components are missing. To enhance the robustness of the detection system, data augmentation techniques are employed during the training phase, allowing the model to generalize well to different circuit layouts and variations in component placements. The trained YOLOv12 model demonstrates its ability to accurately locate missing components, even in the presence of noise, variations in lighting, and different circuit board orientations. Experimental results showcase the effectiveness of the proposed approach, achieving high accuracy and precision in detecting missing components across a range of test scenarios. The system's performance is evaluated on both synthetic datasets and real-world electrical circuits, demonstrating its applicability in practical scenarios. The proposed YOLOv12-based approach holds promise for integration into real-time monitoring systems, contributing to the overall reliability and safety of electrical systems.
Keywords: YOLOv12, electrical circuits, missing components, object detection, PCB inspection, automation, deep learning, anomaly detection, computer vision, fault diagnosis.
| 59 |
Author(s):
Buddha Mokshitha Sree.
Page No : 1-8
|
TriFuseRAG: Tri-Modal Hybrid Retrieval-Augmented Generation Using SQL, Vector, and Graph Databases
Abstract
Retrieval-Augmented Generation (RAG) systems typically route all queries to a single retrieval modality, limiting utility for heterogeneous query types that require simultaneous structured lookup, entity-relationship traversal, and text similarity search. We present TriFuseRAG, a prototype multi-modal RAG system integrating three retrieval backends—a relational SQL engine, an in-memory knowledge graph, and a TF-IDF vector store—under a dual-stage adaptive router and weighted fusion layer. A two-tier safety gate filters adversarial inputs before retrieval. On a 1,126-query closed-world benchmark, the dual-stage router achieves 98.84% route accuracy. In strict no-leakage evaluation, TriFuseRAG achieves 91.3% overall answer accuracy (98.1% on supported queries), and 93.9% on composite Hybrid queries where all single-source baselines score 0%. Overall, TriFuseRAG outperforms the best single-source baseline by 34.7 points (91.3% vs. 26.6%), with p95 query latency under 10ms on CPU hardware. We report full error analysis, latency profiles, and discussion of synthetic evaluation scope.
| 60 |
Author(s):
C.Sennilavazhagan.
Page No : 1-9
|
Stock market analysis and prediction
Abstract
Stock market prediction remains a challenging task due to the highly volatile, non-linear, and dynamic behavior of financial markets, where price movements are influenced by both historical trends and external factors such as news and investor sentiment. This paper presents a hybrid data-driven framework that combines Bidirectional Gated Recurrent Units (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks for accurate stock price prediction, along with Natural Language Processing (NLP)-based sentiment analysis of financial news. The BiLSTM component is designed to capture long-term dependencies in time-series data, while the BiGRU enhances computational efficiency and learns short-term patterns effectively. The bidirectional structure enables the model to leverage contextual information from both past and future sequences, improving predictive performance. Simultaneously, financial news data is collected and processed using NLP techniques, including text preprocessing and sentiment extraction, to quantify market sentiment. These sentiment scores are integrated with historical stock prices and technical indicators to provide enriched input to the hybrid model. Experimental results indicate that the proposed approach significantly improves prediction accuracy and reduces forecasting errors compared to traditional models. The integration of sentiment-aware features enhances the model’s robustness, making it suitable for real-time decision support in dynamic financial environments.
| 61 |
Author(s):
Ashish Lakhimale.
Page No : 1-9
|
SchemeImpactNet: Forecasting and Optimizing MGNREGA Employment Generation Using Machine Learning
Abstract
The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) represents a cornerstone of India’s social security infrastructure, disbursing substantial fiscal resources annually to sustain rural livelihoods. However, traditional management practices within public welfare administration remain largely reactive, relying heavily on historical baselines and manual planning paradigms that exhibit structural limitations in predicting localized demand spikes and dynamic climate disruptions. This paper introduces SchemeImpactNet, a datadriven predictive and optimization framework engineered to transition public welfare administration from reactive management to evidence-based proactive policy intervention. Leveraging an extensive administrative dataset comprising 7,758 districtyear records across 759 districts and 34 states/union territories from 2014–15 to 2024–25, we evaluate a series of rigorous predictive machine learning algorithms under a strictly leakfree temporal validation protocol. Gradient Boosting models achieve a cross-validated mean R2 of 0.9078 (excluding anomaly years), outperforming alternative parametric and non-parametric algorithms. Utilizing these localized predictions, we model a linear programming resource optimization engine using the PuLP framework to maximize public employment outcomes under fixed historical budgetary parameters. Experimental results establish that mathematically optimized resource reallocation across administrative subdivisions yields a simulated employment gain of 1,840.41 lakh person-days, representing a 6.38% net improvement in nationwide execution efficiency without increasing nominal public expenditure. The architecture is containerized and deployed via an open REST API and interactive Streamlit service, establishing reproducible methodology for public policy optimization.
Index Terms—Public Policy Analytics, Predictive Modeling,
Gradient Boosting, Resource Allocation Optimization, Linear Programming, MGNREGA.
| 62 |
Author(s):
Ms. Aastha Sharma.
Page No : 1-11
|
A STUDY ON THE IMPACT OF SHORT-FORM VIDEO CONTENT (REELS/SHORTS) ON THE PURCHASING DECISIONS OF GEN Z CONSUMERS
Abstract
The rapid growth of digital media has significantly transformed consumer behavior, particularly among Generation Z. Short-form video content such as Instagram Reels and YouTube Shorts has emerged as a powerful marketing tool due to its engaging, concise, and visually appealing format. This study aims to analyze the impact of short-form video content on the purchasing decisions of Gen Z consumers. Using a descriptive research design, primary data was collected from 173 respondents through a structured questionnaire. The findings reveal that short-form videos play a significant role in product discovery, influence consumer interest, and moderately impact purchasing decisions. However, factors such as trust in influencers and content credibility vary among individuals. The study highlights the growing importance of short-form video marketing in shaping consumer behavior and provides insights for marketers to design effective strategies.
| 63 |
Author(s):
Juniyad Tamboli.
Page No : 1-11
|
An Intelligent Robotic Access Control System for High-Security Applications
Abstract
Manual verification procedures used in many
restricted and high-security areas are often inefficient and
susceptible to human errors, leading to delays and security
concerns. This work presents a Smart Vehicle Entry
Management System that combines a robotic arm, a high
resolution camera, Optical Character Recognition (OCR), and
real-time image processing to automate the entry verification
process. The robotic arm enables precise positioning of the
camera for capturing the vehicle license plate, driver's face, and
identification documents. A voice input module is also
incorporated to record the purpose of entry. The acquired
information is stored digitally, improving traceability and
eliminating the limitations associated with manual record
maintenance. Experimental results indicate that the proposed
system reduces entry processing time while providing reliable
vehicle and driver identification, thereby enhancing both
operational efficiency and security.
| 64 |
Author(s):
Vicky Kumar.
Page No : 1-12
|
IMPACT OF FINANCIAL MANAGEMENT EFFICIENCY ON PROFITABILITY AND WORKING CAPITAL PERFORMANCE: A CASE STUDY OF AVRO INDIA LTD.
Abstract
Financial management plays an important role in the growth and success of any organization. This study examines how financial management practices influence profitability and working capital performance at Avro India Ltd., a leading manufacturer of plastic furniture in India.
The study was carried out using information collected during the internship period. Data was gathered from company records such as sales reports, debtor statements, distributor records, and financial reports, and analyzed using ratio analysis, trend analysis, and working capital evaluation techniques.
The findings show that the company has maintained steady growth in revenue and profitability through effective financial planning and cost control practices. It was also observed that delays in collecting payments from some distributors affect cash flow and working capital efficiency.
The study concludes that sound financial management practices contribute significantly to improving profitability and maintaining financial stability in the organization.
| 65 |
Author(s):
Abirami. A.
Page No : 1-12
|
COMPARISON ON ARTIFACTS LEFT ON ROOTED AND NON-ROOTED ANDROID DEVICES
Abstract
This project focuses on the comparative analysis of digital forensic artifacts obtained from rooted and non-rooted Android devices. With the rapid growth of mobile usage, smartphones have become a significant source of digital evidence in forensic investigations.
| 66 |
Author(s):
Faizan Ali.
Page No : 1-13
|
“AN ANALYSIS OF CONSUMER BEHAVIORAL PATTERNS IN KFC MARKETING STRATEGIES”
Abstract
This research project investigates the consumer behavioral patterns among KFC customers and analyses how KFC’s marketing strategies influence consumer decisions, preferences, and visit patterns. The study was conducted using a primary research method — a structured questionnaire-based survey of 59 respondents, predominantly college students in the 18–25 age group.
The research examines key dimensions of consumer behavior including demographic profiles, visit frequency, product preferences, promotional sensitivity, competitive positioning, and social media influence. Quantitative data from the survey was analyzed and interpreted to derive meaningful insights.
Key findings reveal that 42.4% of respondents are most influenced by limited time offers, 31% cite taste as the primary decision-making factor, 62.5% of respondents visit KFC six or more times, and social media is the dominant awareness channel for 47.5% of the sample. The Original Recipe is the most preferred at 31%, and 56.4% of consumers visit KFC with friends, positioning KFC strongly as a social dining experience.
Based on these findings, the study recommends that KFC continue to invest in digital and social media marketing, strengthen its combo and value-for-money propositions, and expand vegetarian offerings to capture a broader consumer base.
Keywords: Consumer Behavior, KFC, Marketing Strategies, Fast Food, Brand Preference, Promotional Influence, Social Media Marketing, Product Preference.
| 67 |
Author(s):
ABIOLA TAIWO-TIJANI.
Page No : 1-16
|
THE CONVERGENCE OF KNOWLEDGE MANAGEMENT AND LEAN SIX SIGMA IN MANUFACTURING: A SYSTEMATIC LITERATURE REVIEW
Abstract
This study presents a systematic literature review that examines the integration of Knowledge Management (KM) and Lean Six Sigma methodologies, while answering the defined research questions exploring their application in various organizational contexts and proposing future directions for research and practice especially in the manufacturing sector. By synthesizing existing literature, this review analyses the synergies between KM and Lean Six Sigma, emphasizing their individual input in ensuring organizational excellence through constant improvement, innovation, and advanced decision-making processes. The review also discusses challenges, opportunities, and potential areas for further exploration, shedding light on the evolving landscape of integrated KM and Lean Six Sigma initiatives. The research was conducted in Scopus and Web of Science. From the 52 results, 12 articles were included based on the eligibility criteria. The results show that few scientific studies report the application of both areas. This review evidences many positive effects of this integration, both for organizations and workers. In general, the topic of this review has the potential to be further studied through conceptual studies and case studies in organizations from different sectors to provide broader conclusions and show the relevance of this theme.
| 68 |
Author(s):
Motiullah Abdul Hafiz .
Page No : 1-31
|
A Review of the ANDA vs. NDA Approval Process
Abstract
The U.S. Food and Drug Administration (FDA) relies on two principal regulatory pathways to authorize the
marketing of small-molecule drug products: the New Drug Application (NDA) pathway for innovator products
and the Abbreviated New Drug Application (ANDA) pathway for generic products. Although both pathways are
designed to ensure that marketed medicines meet acceptable standards of safety, quality, and performance, they
differ substantially in legal basis, evidentiary burden, regulatory purpose, and commercial consequence. An
NDA requires a sponsor to establish a drug’s safety and effectiveness through original evidence supported by
preclinical studies, clinical investigations, and comprehensive chemistry, manufacturing, and controls (CMC)
documentation. By contrast, an ANDA allows a sponsor to seek approval of a generic product by relying on the
FDA’s prior finding of safety and effectiveness for a reference listed drug (RLD), provided that the applicant
demonstrates pharmaceutical equivalence, bioequivalence, labeling sameness within permitted limits, and
adequate manufacturing quality. This review critically examines the scientific, legal, and regulatory foundations
of NDA and ANDA approval in the United States. It synthesizes research literature, FDA guidance, and
statutory frameworks to compare application architecture, clinical and comparative evidence requirements,
bioavailability and bioequivalence standards, CMC expectations, patent certification, exclusivity, review
timelines, common causes of delay, and post-approval obligations. The review also discusses the growing
importance of complex generics, the interaction between regulatory approval and market competition, and the
broader public health significance of balancing pharmaceutical innovation with affordability. By examining
NDA and ANDA pathways as complementary components of a single regulatory system rather than isolated
approval mechanisms, this article highlights how the FDA simultaneously supports the development of new
therapies and the expansion of lower-cost generic access.
